Associate Professor William Harcombe

CBS Ecology, Evolution & Behav
College of Biological Sciences
Twin Cities
Project Title: 
Microbial Ecosystem Prediction Using Multi-Species Metabolic Modeling

Natural microbial communities are composed of multiple species which are metabolically connected and whose interactions give rise to important emergent behaviors. For example, many microbes coexist within human guts (the microbiome), and the specific behavior of the microbiome can span from normal functioning to disease states depending on the species composition, the abiotic environment, and the interactions of the microbes with each other and their environment. Other examples of important metabolically connected microbial communities include multi-species biofilm ecosystems on medical devices or in industrial production, communities involved in the biodegredation of harmful chemicals, and soil microbial communities. Predicting how such communities function under different conditions is important yet extremely difficult. The Harcombe lab seeks to understand how a model three-species microbial community functions under different conditions and hopes to gain strong predictive ability with the use of metabolic modeling.

The group conducts many thousands of computer simulations that explore how the model community will perform, with different definitions of performance including biomass production and stability, in different abiotic environments and with different metabolic connectivities. Using their software platform COMETS (Computation of Microbial Ecosystems in Space and Time), they can use the model species' metabolic networks to conduct dynamic flux balance analysis simulations over space and time, which predict how much each metabolic reaction should flux over each time step in order to optimize some objective, often biomass. Then, by manipulating the metabolic networks, for example simulating gene knockouts by forcing flux through a reaction to zero, the researchers can learn how the connectivity within and among the species' metabolic networks causes different emergent behavior.  They can do this in a spatially explicit environment, which is important for answering evolutionary questions and understanding the interplay between physiology and the physical environment.

So far, COMETS has been successfully used to predict growth profiles of the model community in a variety of circumstances, and has been used to predict how single gene knockouts affect the robustness of a two-species model community when the two species either compete for resources or are in an environment where they must cooperate to grow. Additionally, the researchers have conducted many hundreds of spatial simulations.  Furthermore, the researchers began acquiring shotgun sequencing data which they analyzed using bioinformatic pipelines at MSI.

During 2023, the researchers will:

  • Analyze shotgun sequencing results from at least 100 samples
  • Conduct many COMETS simulations in a single well-mixed environment, to examine epistasis among gene knockouts
  • Based upon the results from the COMETS simulations, select a subset of simulations to repeat in a spatially-explicit environment with diffusive spread of cells and metabolites

These results will be of broad interest to researchers interested in the interactions of metabolism, ecology, and genetics. The researchers will obtain a nuanced view of how species metabolism interact with each other to affect a greater ecology, and will hopefully gain a predictive understanding that will give us tools that can be applied to applied ecosystems in future work. 

Project Investigators

Ave Bisesi
Jeremy Chacon
Lisa Fazzino
Sarah Hammarlund
Associate Professor William Harcombe
Jonathan Martinson
Lauren Slavic
Leno Smith Jr
Tsz Fung Wong
Xianyi Xiong
Are you a member of this group? Log in to see more information.